• Energy Research
• Open Access close

202208 bckw ; Version of Record ; Others ; U.S. Environmental Protection Agency; South Coast Air Quality Management District ; Published

Passive cooling devices take advantage of the partially transparent properties of the atmosphere in the longwave spectral band from 8 to 13 μm (the so-called “atmospheric window”) to reject radiation to outer space. Spectrally designed thermophotonic devices have raised substantial attention recently for their potential to provide passive and carbon-free alternatives to air conditioning. However, the level of transparency of the atmospheric window depends on the local content of water vapor in the atmosphere and on the optical depth of clouds in the local sky. Thus, the radiative cooling capacity of solar reflectors not only depends on the optical properties of their surfaces but also on local meteorological conditions. In this work, detailed radiative cooling resource maps for the contiguous United States are presented with the goal of determining the best climates for large-scale deployment of passive radiative cooling technologies. The passive cooling potential is estimated based on ideal optical properties, i.e., zero shortwave absorptance (maximum reflectance) and blackbody longwave emittance. Both annual and season-averaged maps are presented. Daytime and nighttime cooling potential are also computed and compared. The annual average cooling potential over the contiguous United States is 50.5 m−2. The southwestern United States has the highest annual averaged cooling potential, over 70 W m−2, due to its dry and mostly clear sky meteorological conditions. The southeastern United States has the lowest potential, around 30 W m−2, due to frequent humid and/or overcast weather conditions. In the spring and fall months, the Arizona and New Mexico climates provide the highest passive cooling potential, while in the summer months, Nevada and Utah exhibit higher potentials. Passive radiative cooling is primarily effective in the western United States, while it is mostly ineffective in humid and overcast climates elsewhere.

Estimating spectral plane-of-array (POA) solar irradiance on inclined surfaces is an important step in the design and performance evaluation of both photovoltaic and concentrated solar plants. This work introduces a fast, line-by-line spectral, Monte Carlo (MC) radiative transfer model approach to simulate anisotropic distributions of shortwave radiation through the atmosphere as photon bundles impinge on inclined surfaces. This fast Monte Carlo approach reproduces the angular distribution of solar irradiance without the undesirable effects of spatial discretization and thus computes detailed POA irradiance values on surfaces at any orientation and also when surfaces are subjected to the anisotropic ground and atmospheric scattering. Polarization effects are also easily incorporated into this approach that can be considered as direct numerical simulation of the physics involved. Here, we compare our Monte Carlo radiative transfer model with the most widely used empirical transposition model, Perez4, under various conditions. The results show that the Perez4 model reproduces the more detailed Monte Carlo simulations with less than 10% deviation under clear skies for all relevant surface tilt and azimuth angles. When optically thin clouds are present, observed deviations are larger, especially when the receiving surface is strongly tilted. Deviations are also observed for large azimuth angle differences between the receiving surface and the solar position. When optically thick clouds are present, the two models agree within 15% deviation for nearly all surface orientation and tilt angles. The overall deviations are smaller when compared with cases for optically thin clouds. The Perez4 model performs very well (∼6.0% deviation) in comparison with the detailed MC simulations for all cases, thus validating its widespread use for practical solar applications. When detailed atmospheric profiles and cloud optical properties are available, the proposed fast Monte Carlo radiative model reproduces accurate spectral and angular POA irradiance levels for various atmospheric and cloud cover conditions, surface orientations, and different surface and ground properties.

The output of ground-based, solar power generation systems is strongly dependent on cloud cover, which is the main contributor to solar power variability and uncertainty. Cloud optical properties are typically over-simplified in forecasting applications due to the lack of real-time, accurate estimates. In this work, we introduce a method, the Spectral Cloud Optical Property Estimation (SCOPE), for estimating cloud optical properties directly from high-resolution (5-min, 2 km) imagery from Geostationary Operational Environmental Satellite (GOES)-R, which is the newest generation of the GOES system. The SCOPE method couples a two-stream, spectrally resolved radiative model with the longwave GOES-R sensor output to simultaneously estimate the cloud optical depth, cloud top height, and cloud thickness during both day and night at 5-min intervals. The accuracy of SCOPE is evaluated using one year (2018) of downwelling longwave (DLW) radiation measurements from the Surface Radiation Budget Network, which consists of seven sites spread across climatically diverse regions of the contiguous United States. During daytime clear-sky periods, SCOPE predicts DLW within instrument uncertainty (10 W m−2) for four of the seven locations, with the remaining locations yielding errors of the order of 11.2, 17.7, and 20.2 W m−2. For daytime cloudy-sky, daytime all-sky (clear or cloudy), and nighttime all-sky periods, SCOPE achieves root mean square error values of 23.0–34.5 W m−2 for all seven locations. These results, together with the low-latency of the method (∼1 s per sample), show that SCOPE provides a viable solution to real-time, accurate estimation of cloud optical properties for both day and night.

The integration of a solid oxide electrolysis cell (SOEC) with a photovoltaic (PV) system presents a viable method for storing variable solar energy through the production of green hydrogen. To ensure the SOEC's safety and longevity amidst dramatic fluctuations in solar power, control strategies are needed to limit the temperature gradients and rates of temperature change within the SOEC. Recognizing that the reactant supply influences the current, a novel control strategy is developed to modulate heat generation in the SOEC by adjusting the fuel flow rate. The effectiveness of this strategy is assessed through numerical simulations conducted on a coupled PV-SOEC system using actual solar irradiance data, recorded at two-second intervals, to account for rapid changes in solar exposure. The results indicate that conventional control strategies, which increase airflow rates, are inadequate in effectively suppressing the rate of temperature variation in scenarios of drastic solar power changes. In contrast, our proposed strategy demonstrates successful management of the SOEC's heat generation, thereby reducing the temperature gradient and rate of variation within the SOEC to below 5 K/cm and 1 K/min, respectively.

AbstractA comprehensive understanding of the transient characteristics in solid oxide cells (SOCs) is crucial for advancing SOC technology in renewable energy storage and conversion. However, general formulas describing the relationship between SOC transients and multiple parameters remain elusive. Through comprehensive numerical analysis, we find that the thermal and gaseous response times of SOCs upon rapid electrical variations are on the order of two characteristic times (τh and τm), respectively. The gaseous response time is approximately 1τm, and the thermal response time aligns with roughly 2τh. These characteristic times represent the overall heat and mass transfer rates within the cell, and their mathematical relationships with various SOC design and operating parameters are revealed. Validation of τh and τm is achieved through comparison with an in-house experiment and existing literature data, achieving the same order of magnitude for a wide range of electrochemical cells, showcasing their potential use for characterizing transient behaviors in a wide range of electrochemical cells. Moreover, two examples are presented to demonstrate how these characteristic times can streamline SOC design and control without the need for complex numerical simulations, thus offering valuable insights and tools for enhancing the efficiency and durability of electrochemical cells.